MahaVar: OOD Detection via Class-wise Mahalanobis Distance Variance under Neural Collapse

ArXi:2605.14413v1 Announce Type: new Out-of-distribution (OOD) detection is a critical component for ensuring the reliability of deep neural networks in safety-critical applications. In this work, we present a key empirical observation: for in-distribution (ID) samples, class-wise Mahalanobis distances exhibit a pronounced sharp minimum structure, where the distance to the nearest class is small while distances to all other classes remain large, resulting in high variance across classes.